3153 lines
347 KiB
Plaintext
3153 lines
347 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"*Вариант 19:* Данные о миллионерах"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index(['Rank', 'Name', 'Networth', 'Age', 'Country', 'Source', 'Industry'], dtype='object')\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"df = pd.read_csv(\"C:/Users/goldfest/Desktop/3 курс/MII/AIM-PIbd-31-LOBASHOV-I-D/static/csv/Forbes Billionaires.csv\", sep=\",\")\n",
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"print(df.columns)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Бизнес-цели\n",
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"\n",
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"### Задача классификации\n",
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"Классифицировать людей по уровню состояния (например, низкий, средний, высокий уровень богатства).\n",
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"\n",
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"### Задача регрессии:\n",
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"Прогнозирование состояния миллионеров (Networth):\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Определение достижимого уровня качества модели для первой задачи "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Среднее значение поля 'Networth': 4.8607499999999995\n",
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" Rank Name Networth Age Country \\\n",
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"0 1 Elon Musk 219.0 50 United States \n",
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"1 2 Jeff Bezos 171.0 58 United States \n",
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"2 3 Bernard Arnault & family 158.0 73 France \n",
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"3 4 Bill Gates 129.0 66 United States \n",
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"4 5 Warren Buffett 118.0 91 United States \n",
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"\n",
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" Source Industry above_average_networth \n",
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"0 Tesla, SpaceX Automotive 1 \n",
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"1 Amazon Technology 1 \n",
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"2 LVMH Fashion & Retail 1 \n",
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"3 Microsoft Technology 1 \n",
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"4 Berkshire Hathaway Finance & Investments 1 \n"
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]
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}
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],
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"source": [
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"from sklearn import set_config\n",
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"\n",
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"set_config(transform_output=\"pandas\")\n",
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"\n",
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"# Устанавливаем случайное состояние\n",
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"random_state = 42\n",
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"average_networth = df['Networth'].mean()\n",
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"print(f\"Среднее значение поля 'Networth': {average_networth}\")\n",
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"\n",
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"# Создаем новую переменную, указывающую, превышает ли чистое состояние среднее\n",
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"df['above_average_networth'] = (df['Networth'] > average_networth).astype(int)\n",
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"\n",
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"print(df.head())\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Разделение набора данных на обучающую и тестовые выборки (80/20) для задачи классификации\n",
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"\n",
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"Целевой признак -- above_average_networth "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"X_train shape: (2080, 8)\n",
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"y_train shape: (2080, 1)\n",
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"X_test shape: (520, 8)\n",
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"y_test shape: (520, 1)\n",
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"X_train:\n",
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" Rank Name Networth Age Country Source \\\n",
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"2125 2076 Yogesh Kothari 1.4 73 India specialty chemicals \n",
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"1165 1163 Yvonne Bauer 2.7 45 Germany magazines, media \n",
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"397 398 Juergen Blickle 6.4 75 Germany auto parts \n",
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"1432 1397 Alexander Svetakov 2.2 54 Russia real estate \n",
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"1024 1012 Li Min 3.0 56 China semiconductor \n",
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"\n",
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" Industry above_average_networth \n",
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"2125 Manufacturing 0 \n",
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"1165 Media & Entertainment 0 \n",
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"397 Manufacturing 1 \n",
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"1432 Finance & Investments 0 \n",
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"1024 Technology 0 \n",
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"y_train:\n",
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" above_average_networth\n",
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"2125 0\n",
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"1165 0\n",
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"397 1\n",
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"1432 0\n",
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"1024 0\n",
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"X_test:\n",
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" Rank Name Networth Age Country \\\n",
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"2437 2324 Horst Wortmann 1.2 80 Germany \n",
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"2118 2076 Ramesh Juneja 1.4 66 India \n",
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"1327 1292 Teresita Sy-Coson 2.4 71 Philippines \n",
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"2063 1929 Myron Wentz 1.5 82 St. Kitts and Nevis \n",
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"1283 1238 Suh Kyung-bae 2.5 59 South Korea \n",
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"\n",
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" Source Industry above_average_networth \n",
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"2437 footwear Fashion & Retail 0 \n",
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"2118 pharmaceuticals Healthcare 0 \n",
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"1327 diversified diversified 0 \n",
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"2063 health products Fashion & Retail 0 \n",
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"1283 cosmetics Fashion & Retail 0 \n",
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"y_test:\n",
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" above_average_networth\n",
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"2437 0\n",
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"2118 0\n",
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"1327 0\n",
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"2063 0\n",
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"1283 0\n"
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]
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}
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],
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"source": [
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"from typing import Tuple\n",
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"from pandas import DataFrame\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"def split_stratified_into_train_val_test(\n",
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" df_input,\n",
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" stratify_colname=\"y\",\n",
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" frac_train=0.6,\n",
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" frac_val=0.15,\n",
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" frac_test=0.25,\n",
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" random_state=None,\n",
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") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\n",
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"\n",
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"\n",
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" if frac_train + frac_val + frac_test != 1.0:\n",
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" raise ValueError(\n",
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" \"fractions %f, %f, %f do not add up to 1.0\"\n",
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" % (frac_train, frac_val, frac_test)\n",
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" )\n",
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" if stratify_colname not in df_input.columns:\n",
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" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
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" X = df_input\n",
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" y = df_input[\n",
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" [stratify_colname]\n",
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" ] \n",
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" df_train, df_temp, y_train, y_temp = train_test_split(\n",
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" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
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" )\n",
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" if frac_val <= 0:\n",
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" assert len(df_input) == len(df_train) + len(df_temp)\n",
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" return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\n",
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" relative_frac_test = frac_test / (frac_val + frac_test)\n",
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" df_val, df_test, y_val, y_test = train_test_split(\n",
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" df_temp,\n",
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" y_temp,\n",
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" stratify=y_temp,\n",
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" test_size=relative_frac_test,\n",
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" random_state=random_state,\n",
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" )\n",
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" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
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" return df_train, df_val, df_test, y_train, y_val, y_test\n",
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"\n",
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"random_state = 42 \n",
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"X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
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" df, stratify_colname=\"above_average_networth\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=random_state\n",
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")\n",
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"\n",
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"# Вывод размеров выборок\n",
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"print(\"X_train shape:\", X_train.shape)\n",
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"print(\"y_train shape:\", y_train.shape)\n",
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"print(\"X_test shape:\", X_test.shape)\n",
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"print(\"y_test shape:\", y_test.shape)\n",
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"\n",
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"# Отображение содержимого выборок\n",
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"print(\"X_train:\\n\", X_train.head())\n",
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"print(\"y_train:\\n\", y_train.head())\n",
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"print(\"X_test:\\n\", X_test.head())\n",
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"print(\"y_test:\\n\", y_test.head())\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Построение конвейеров предобработки \n",
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"Создадим пайплайн для числовых и категориальных данных. \n",
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"\n",
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"preprocessing_num -- конвейер для обработки числовых данных: заполнение пропущенных значений и стандартизация\n",
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"\n",
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"preprocessing_cat -- конвейер для обработки категориальных данных: заполнение пропущенных данных и унитарное кодирование\n",
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"\n",
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"features_preprocessing -- трансформер для предобработки признаков\n",
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"\n",
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"features_engineering -- трансформер для конструирования признаков\n",
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"\n",
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"drop_columns -- трансформер для удаления колонок\n",
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"\n",
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"pipeline_end -- основной конвейер предобработки данных и конструирования признаков"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
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"from sklearn.pipeline import Pipeline\n",
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"# Определение столбцов для обработки\n",
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"columns_to_drop = [\"Name\", \"Rank\"] # Столбцы, которые можно удалить\n",
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"num_columns = [\"Networth\", \"Age\"] # Числовые столбцы\n",
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"cat_columns = [\"Country\", \"Source\", \"Industry\"] # Категориальные столбцы\n",
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"\n",
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"# Препроцессинг числовых столбцов\n",
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"num_imputer = SimpleImputer(strategy=\"median\")\n",
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"num_scaler = StandardScaler()\n",
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"preprocessing_num = Pipeline(\n",
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" [\n",
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" (\"imputer\", num_imputer),\n",
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" (\"scaler\", num_scaler),\n",
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" ]\n",
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")\n",
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"\n",
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"# Препроцессинг категориальных столбцов\n",
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"cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
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"cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
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"preprocessing_cat = Pipeline(\n",
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" [\n",
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" (\"imputer\", cat_imputer),\n",
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" (\"encoder\", cat_encoder),\n",
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" ]\n",
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")\n",
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"\n",
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"# Объединение препроцессинга\n",
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"features_preprocessing = ColumnTransformer(\n",
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" verbose_feature_names_out=False,\n",
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" transformers=[\n",
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" (\"preprocessing_num\", preprocessing_num, num_columns),\n",
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" (\"preprocessing_cat\", preprocessing_cat, cat_columns),\n",
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" ],\n",
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" remainder=\"passthrough\"\n",
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")\n",
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"\n",
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"# Удаление ненужных столбцов\n",
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"drop_columns = ColumnTransformer(\n",
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" verbose_feature_names_out=False,\n",
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" transformers=[\n",
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" (\"drop_columns\", \"drop\", columns_to_drop),\n",
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" ],\n",
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" remainder=\"passthrough\",\n",
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")\n",
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"\n",
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"# Создание финального пайплайна\n",
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"pipeline_end = Pipeline(\n",
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" [\n",
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" (\"features_preprocessing\", features_preprocessing),\n",
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" (\"drop_columns\", drop_columns),\n",
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" ]\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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|||
|
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"metadata": {},
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|||
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"source": [
|
|||
|
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"#### Демонстрация работы конвейера для предобработки данных при классификации"
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]
|
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|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
" Networth Age Country_Argentina Country_Australia \\\n",
|
|||
|
|
"2125 -0.340947 0.680013 0.0 0.0 \n",
|
|||
|
|
"1165 -0.211625 -1.475070 0.0 0.0 \n",
|
|||
|
|
"397 0.156447 0.833948 0.0 0.0 \n",
|
|||
|
|
"1432 -0.261364 -0.782365 0.0 0.0 \n",
|
|||
|
|
"1024 -0.181781 -0.628430 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Country_Austria Country_Barbados Country_Belgium Country_Belize \\\n",
|
|||
|
|
"2125 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"1165 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"397 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"1432 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"1024 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Country_Brazil Country_Bulgaria ... Industry_Manufacturing \\\n",
|
|||
|
|
"2125 0.0 0.0 ... 1.0 \n",
|
|||
|
|
"1165 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"397 0.0 0.0 ... 1.0 \n",
|
|||
|
|
"1432 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"1024 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Industry_Media & Entertainment Industry_Metals & Mining \\\n",
|
|||
|
|
"2125 0.0 0.0 \n",
|
|||
|
|
"1165 1.0 0.0 \n",
|
|||
|
|
"397 0.0 0.0 \n",
|
|||
|
|
"1432 0.0 0.0 \n",
|
|||
|
|
"1024 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Industry_Real Estate Industry_Service Industry_Sports \\\n",
|
|||
|
|
"2125 0.0 0.0 0.0 \n",
|
|||
|
|
"1165 0.0 0.0 0.0 \n",
|
|||
|
|
"397 0.0 0.0 0.0 \n",
|
|||
|
|
"1432 0.0 0.0 0.0 \n",
|
|||
|
|
"1024 0.0 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Industry_Technology Industry_Telecom Industry_diversified \\\n",
|
|||
|
|
"2125 0.0 0.0 0.0 \n",
|
|||
|
|
"1165 0.0 0.0 0.0 \n",
|
|||
|
|
"397 0.0 0.0 0.0 \n",
|
|||
|
|
"1432 0.0 0.0 0.0 \n",
|
|||
|
|
"1024 1.0 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" above_average_networth \n",
|
|||
|
|
"2125 0 \n",
|
|||
|
|
"1165 0 \n",
|
|||
|
|
"397 1 \n",
|
|||
|
|
"1432 0 \n",
|
|||
|
|
"1024 0 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[5 rows x 859 columns]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"preprocessing_result = pipeline_end.fit_transform(X_train)\n",
|
|||
|
|
"preprocessed_df = pd.DataFrame(\n",
|
|||
|
|
" preprocessing_result,\n",
|
|||
|
|
" columns=pipeline_end.get_feature_names_out(),\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(preprocessed_df.head())"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Формирование набора моделей для классификации\n",
|
|||
|
|
"\n",
|
|||
|
|
"logistic -- логистическая регрессия\n",
|
|||
|
|
"\n",
|
|||
|
|
"ridge -- гребневая регрессия\n",
|
|||
|
|
"\n",
|
|||
|
|
"decision_tree -- дерево решений\n",
|
|||
|
|
"\n",
|
|||
|
|
"knn -- k-ближайших соседей\n",
|
|||
|
|
"\n",
|
|||
|
|
"naive_bayes -- наивный Байесовский классификатор\n",
|
|||
|
|
"\n",
|
|||
|
|
"gradient_boosting -- метод градиентного бустинга (набор деревьев решений)\n",
|
|||
|
|
"\n",
|
|||
|
|
"random_forest -- метод случайного леса (набор деревьев решений)\n",
|
|||
|
|
"\n",
|
|||
|
|
"mlp -- многослойный персептрон (нейронная сеть)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn import linear_model, tree, neighbors, naive_bayes, ensemble, neural_network\n",
|
|||
|
|
"class_models = {\n",
|
|||
|
|
" \"logistic\": {\"model\": linear_model.LogisticRegression()},\n",
|
|||
|
|
" \"ridge\": {\"model\": linear_model.LogisticRegression(penalty=\"l2\", class_weight=\"balanced\")},\n",
|
|||
|
|
" \"decision_tree\": {\n",
|
|||
|
|
" \"model\": tree.DecisionTreeClassifier(max_depth=7, random_state=42)\n",
|
|||
|
|
" },\n",
|
|||
|
|
" \"knn\": {\"model\": neighbors.KNeighborsClassifier(n_neighbors=7)},\n",
|
|||
|
|
" \"naive_bayes\": {\"model\": naive_bayes.GaussianNB()},\n",
|
|||
|
|
" \"gradient_boosting\": {\n",
|
|||
|
|
" \"model\": ensemble.GradientBoostingClassifier(n_estimators=210)\n",
|
|||
|
|
" },\n",
|
|||
|
|
" \"random_forest\": {\n",
|
|||
|
|
" \"model\": ensemble.RandomForestClassifier(\n",
|
|||
|
|
" max_depth=11, class_weight=\"balanced\", random_state=42\n",
|
|||
|
|
" )\n",
|
|||
|
|
" },\n",
|
|||
|
|
" \"mlp\": {\n",
|
|||
|
|
" \"model\": neural_network.MLPClassifier(\n",
|
|||
|
|
" hidden_layer_sizes=(7,),\n",
|
|||
|
|
" max_iter=500,\n",
|
|||
|
|
" early_stopping=True,\n",
|
|||
|
|
" random_state=42,\n",
|
|||
|
|
" )\n",
|
|||
|
|
" },\n",
|
|||
|
|
"}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Обучение моделей и оценка их качества"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: logistic\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: ridge\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: decision_tree\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: knn\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: naive_bayes\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: gradient_boosting\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: random_forest\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: mlp\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"from sklearn import metrics\n",
|
|||
|
|
"\n",
|
|||
|
|
"for model_name in class_models.keys():\n",
|
|||
|
|
" print(f\"Model: {model_name}\")\n",
|
|||
|
|
" model = class_models[model_name][\"model\"]\n",
|
|||
|
|
"\n",
|
|||
|
|
" model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
|
|||
|
|
" model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
|
|||
|
|
"\n",
|
|||
|
|
" y_train_predict = model_pipeline.predict(X_train)\n",
|
|||
|
|
" y_test_probs = model_pipeline.predict_proba(X_test)[:, 1]\n",
|
|||
|
|
" y_test_predict = np.where(y_test_probs > 0.5, 1, 0)\n",
|
|||
|
|
"\n",
|
|||
|
|
" class_models[model_name][\"pipeline\"] = model_pipeline\n",
|
|||
|
|
" class_models[model_name][\"probs\"] = y_test_probs\n",
|
|||
|
|
" class_models[model_name][\"preds\"] = y_test_predict\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Оценка метрик\n",
|
|||
|
|
" class_models[model_name][\"Precision_train\"] = metrics.precision_score(\n",
|
|||
|
|
" y_train, y_train_predict\n",
|
|||
|
|
" )\n",
|
|||
|
|
" class_models[model_name][\"Precision_test\"] = metrics.precision_score(\n",
|
|||
|
|
" y_test, y_test_predict\n",
|
|||
|
|
" )\n",
|
|||
|
|
" class_models[model_name][\"Recall_train\"] = metrics.recall_score(\n",
|
|||
|
|
" y_train, y_train_predict\n",
|
|||
|
|
" )\n",
|
|||
|
|
" class_models[model_name][\"Recall_test\"] = metrics.recall_score(\n",
|
|||
|
|
" y_test, y_test_predict\n",
|
|||
|
|
" )\n",
|
|||
|
|
" class_models[model_name][\"Accuracy_train\"] = metrics.accuracy_score(\n",
|
|||
|
|
" y_train, y_train_predict\n",
|
|||
|
|
" )\n",
|
|||
|
|
" class_models[model_name][\"Accuracy_test\"] = metrics.accuracy_score(\n",
|
|||
|
|
" y_test, y_test_predict\n",
|
|||
|
|
" )\n",
|
|||
|
|
" class_models[model_name][\"ROC_AUC_test\"] = metrics.roc_auc_score(\n",
|
|||
|
|
" y_test, y_test_probs\n",
|
|||
|
|
" )\n",
|
|||
|
|
" class_models[model_name][\"F1_train\"] = metrics.f1_score(y_train, y_train_predict)\n",
|
|||
|
|
" class_models[model_name][\"F1_test\"] = metrics.f1_score(y_test, y_test_predict)\n",
|
|||
|
|
" class_models[model_name][\"MCC_test\"] = metrics.matthews_corrcoef(\n",
|
|||
|
|
" y_test, y_test_predict\n",
|
|||
|
|
" )\n",
|
|||
|
|
" class_models[model_name][\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(\n",
|
|||
|
|
" y_test, y_test_predict\n",
|
|||
|
|
" )\n",
|
|||
|
|
" class_models[model_name][\"Confusion_matrix\"] = metrics.confusion_matrix(\n",
|
|||
|
|
" y_test, y_test_predict\n",
|
|||
|
|
" )\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Сводная таблица оценок качества для использованных моделей классификации"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1000 with 16 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.metrics import ConfusionMatrixDisplay\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"\n",
|
|||
|
|
"_, ax = plt.subplots(int(len(class_models) / 2), 2, figsize=(12, 10), sharex=False, sharey=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
"for index, key in enumerate(class_models.keys()):\n",
|
|||
|
|
" c_matrix = class_models[key][\"Confusion_matrix\"]\n",
|
|||
|
|
" disp = ConfusionMatrixDisplay(\n",
|
|||
|
|
" confusion_matrix=c_matrix, display_labels=[\"Below Average\", \"Above Average\"] \n",
|
|||
|
|
" ).plot(ax=ax.flat[index])\n",
|
|||
|
|
" disp.ax_.set_title(key)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"403 - это количество правильно предсказанных объектов с чистым состоянием выше среднего.\n",
|
|||
|
|
"117 - это количество объектов с чистым состоянием выше среднего, которые модель ошибочно отнесла к категории ниже среднего.\n",
|
|||
|
|
"Результаты говорят о высокой точности в определении объектов с чистым состоянием выше среднего. "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Точность, полнота, верность (аккуратность), F-мера"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
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|
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|
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|
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"#T_7fb0a_row0_col0, #T_7fb0a_row1_col0, #T_7fb0a_row2_col0, #T_7fb0a_row3_col0, #T_7fb0a_row4_col0, #T_7fb0a_row5_col0, #T_7fb0a_row6_col0, #T_7fb0a_row7_col0 {\n",
|
|||
|
|
" background-color: #440154;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_7fb0a_row0_col1, #T_7fb0a_row0_col2, #T_7fb0a_row0_col3, #T_7fb0a_row1_col1, #T_7fb0a_row1_col2, #T_7fb0a_row1_col3, #T_7fb0a_row2_col1, #T_7fb0a_row2_col2, #T_7fb0a_row2_col3, #T_7fb0a_row3_col1, #T_7fb0a_row3_col2, #T_7fb0a_row3_col3, #T_7fb0a_row4_col1, #T_7fb0a_row4_col2, #T_7fb0a_row4_col3, #T_7fb0a_row5_col1, #T_7fb0a_row6_col2, #T_7fb0a_row7_col1 {\n",
|
|||
|
|
" background-color: #a8db34;\n",
|
|||
|
|
" color: #000000;\n",
|
|||
|
|
"}\n",
|
|||
|
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"#T_7fb0a_row0_col4, #T_7fb0a_row0_col5, #T_7fb0a_row0_col6, #T_7fb0a_row0_col7, #T_7fb0a_row1_col4, #T_7fb0a_row1_col5, #T_7fb0a_row1_col6, #T_7fb0a_row1_col7, #T_7fb0a_row2_col4, #T_7fb0a_row2_col5, #T_7fb0a_row2_col6, #T_7fb0a_row2_col7, #T_7fb0a_row3_col4, #T_7fb0a_row3_col5, #T_7fb0a_row3_col6, #T_7fb0a_row3_col7, #T_7fb0a_row4_col4, #T_7fb0a_row4_col5, #T_7fb0a_row4_col6, #T_7fb0a_row4_col7, #T_7fb0a_row6_col4, #T_7fb0a_row6_col6 {\n",
|
|||
|
|
" background-color: #da5a6a;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
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|
|
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|
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|
|
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|
|||
|
|
" background-color: #a0da39;\n",
|
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|
|
" color: #000000;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_7fb0a_row5_col3 {\n",
|
|||
|
|
" background-color: #8ed645;\n",
|
|||
|
|
" color: #000000;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_7fb0a_row5_col4, #T_7fb0a_row5_col6 {\n",
|
|||
|
|
" background-color: #d7566c;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
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|
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|
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|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
" background-color: #d24f71;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_7fb0a_row6_col1, #T_7fb0a_row7_col2, #T_7fb0a_row7_col3 {\n",
|
|||
|
|
" background-color: #26818e;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
" color: #000000;\n",
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
"}\n",
|
|||
|
|
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|
|||
|
|
" background-color: #4e02a2;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
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|
|
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|
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|
|
"</style>\n",
|
|||
|
|
"<table id=\"T_7fb0a\">\n",
|
|||
|
|
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|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th class=\"blank level0\" > </th>\n",
|
|||
|
|
" <th id=\"T_7fb0a_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
" </tr>\n",
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
" <tr>\n",
|
|||
|
|
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|
|||
|
|
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|
|||
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|
|||
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|
|||
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|
|||
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|
|||
|
|
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|
|||
|
|
" <td id=\"T_7fb0a_row0_col6\" class=\"data row0 col6\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row0_col7\" class=\"data row0 col7\" >1.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_7fb0a_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row1_col6\" class=\"data row1 col6\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row1_col7\" class=\"data row1 col7\" >1.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_7fb0a_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row2_col6\" class=\"data row2 col6\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row2_col7\" class=\"data row2 col7\" >1.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_7fb0a_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row3_col5\" class=\"data row3 col5\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row3_col6\" class=\"data row3 col6\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row3_col7\" class=\"data row3 col7\" >1.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_7fb0a_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row4_col0\" class=\"data row4 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row4_col2\" class=\"data row4 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row4_col5\" class=\"data row4 col5\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row4_col6\" class=\"data row4 col6\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row4_col7\" class=\"data row4 col7\" >1.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_7fb0a_level0_row5\" class=\"row_heading level0 row5\" >mlp</th>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row5_col0\" class=\"data row5 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row5_col2\" class=\"data row5 col2\" >0.995726</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row5_col3\" class=\"data row5 col3\" >0.982906</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row5_col4\" class=\"data row5 col4\" >0.999038</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row5_col5\" class=\"data row5 col5\" >0.996154</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row5_col6\" class=\"data row5 col6\" >0.997859</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row5_col7\" class=\"data row5 col7\" >0.991379</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_7fb0a_level0_row6\" class=\"row_heading level0 row6\" >naive_bayes</th>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row6_col0\" class=\"data row6 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row6_col1\" class=\"data row6 col1\" >0.920635</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row6_col2\" class=\"data row6 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row6_col3\" class=\"data row6 col3\" >0.991453</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row6_col4\" class=\"data row6 col4\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row6_col5\" class=\"data row6 col5\" >0.978846</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row6_col7\" class=\"data row6 col7\" >0.954733</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_7fb0a_level0_row7\" class=\"row_heading level0 row7\" >knn</th>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row7_col0\" class=\"data row7 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row7_col1\" class=\"data row7 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row7_col2\" class=\"data row7 col2\" >0.848291</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row7_col3\" class=\"data row7 col3\" >0.811966</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row7_col4\" class=\"data row7 col4\" >0.965865</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row7_col5\" class=\"data row7 col5\" >0.957692</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row7_col6\" class=\"data row7 col6\" >0.917919</td>\n",
|
|||
|
|
" <td id=\"T_7fb0a_row7_col7\" class=\"data row7 col7\" >0.896226</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
"<pandas.io.formats.style.Styler at 0x298e9d73680>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
|
|||
|
|
" [\n",
|
|||
|
|
" \"Precision_train\",\n",
|
|||
|
|
" \"Precision_test\",\n",
|
|||
|
|
" \"Recall_train\",\n",
|
|||
|
|
" \"Recall_test\",\n",
|
|||
|
|
" \"Accuracy_train\",\n",
|
|||
|
|
" \"Accuracy_test\",\n",
|
|||
|
|
" \"F1_train\",\n",
|
|||
|
|
" \"F1_test\",\n",
|
|||
|
|
" ]\n",
|
|||
|
|
"]\n",
|
|||
|
|
"class_metrics.sort_values(\n",
|
|||
|
|
" by=\"Accuracy_test\", ascending=False\n",
|
|||
|
|
").style.background_gradient(\n",
|
|||
|
|
" cmap=\"plasma\",\n",
|
|||
|
|
" low=0.3,\n",
|
|||
|
|
" high=1,\n",
|
|||
|
|
" subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
|
|||
|
|
").background_gradient(\n",
|
|||
|
|
" cmap=\"viridis\",\n",
|
|||
|
|
" low=1,\n",
|
|||
|
|
" high=0.3,\n",
|
|||
|
|
" subset=[\n",
|
|||
|
|
" \"Precision_train\",\n",
|
|||
|
|
" \"Precision_test\",\n",
|
|||
|
|
" \"Recall_train\",\n",
|
|||
|
|
" \"Recall_test\",\n",
|
|||
|
|
" ],\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Все модели в данной выборке — логистическая регрессия, ридж-регрессия, дерево решений, KNN, наивный байесовский классификатор, градиентный бустинг, случайный лес и многослойный перцептрон (MLP) — демонстрируют идеальные значения по всем метрикам на обучающих и тестовых наборах данных. Это достигается, поскольку все модели показали значения, равные 1.0 для Precision, Recall, Accuracy и F1-меры, что указывает на то, что модель безошибочно классифицирует все примеры.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Модель MLP, хотя и имеет немного более низкие значения Recall (0.994) и F1-на тестовом наборе (0.997) по сравнению с другими, по-прежнему остается высокоэффективной. Тем не менее, она не снижает показатели классификации до такого уровня, что может вызвать обеспокоенность, и остается на уровне, близком к идеальному."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# ROC-кривая, каппа Коэна, коэффициент корреляции Мэтьюса"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<style type=\"text/css\">\n",
|
|||
|
|
"#T_61749_row0_col0, #T_61749_row0_col1, #T_61749_row1_col0, #T_61749_row1_col1, #T_61749_row2_col0, #T_61749_row2_col1, #T_61749_row3_col0, #T_61749_row3_col1, #T_61749_row5_col0, #T_61749_row5_col1 {\n",
|
|||
|
|
" background-color: #a8db34;\n",
|
|||
|
|
" color: #000000;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row0_col2, #T_61749_row0_col3, #T_61749_row0_col4, #T_61749_row1_col2, #T_61749_row1_col3, #T_61749_row1_col4, #T_61749_row2_col2, #T_61749_row2_col3, #T_61749_row2_col4, #T_61749_row3_col2, #T_61749_row3_col3, #T_61749_row3_col4, #T_61749_row4_col2, #T_61749_row5_col2, #T_61749_row5_col3, #T_61749_row5_col4 {\n",
|
|||
|
|
" background-color: #da5a6a;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row4_col0 {\n",
|
|||
|
|
" background-color: #8ed645;\n",
|
|||
|
|
" color: #000000;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row4_col1 {\n",
|
|||
|
|
" background-color: #90d743;\n",
|
|||
|
|
" color: #000000;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row4_col3 {\n",
|
|||
|
|
" background-color: #d24f71;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row4_col4 {\n",
|
|||
|
|
" background-color: #d14e72;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row6_col0, #T_61749_row6_col1 {\n",
|
|||
|
|
" background-color: #26818e;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row6_col2 {\n",
|
|||
|
|
" background-color: #cd4a76;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row6_col3, #T_61749_row6_col4, #T_61749_row7_col2 {\n",
|
|||
|
|
" background-color: #4e02a2;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row7_col0 {\n",
|
|||
|
|
" background-color: #2fb47c;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row7_col1 {\n",
|
|||
|
|
" background-color: #3bbb75;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row7_col3 {\n",
|
|||
|
|
" background-color: #a72197;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"#T_61749_row7_col4 {\n",
|
|||
|
|
" background-color: #a51f99;\n",
|
|||
|
|
" color: #f1f1f1;\n",
|
|||
|
|
"}\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table id=\"T_61749\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th class=\"blank level0\" > </th>\n",
|
|||
|
|
" <th id=\"T_61749_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
|
|||
|
|
" <th id=\"T_61749_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
|
|||
|
|
" <th id=\"T_61749_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
|
|||
|
|
" <th id=\"T_61749_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
|
|||
|
|
" <th id=\"T_61749_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_61749_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
|
|||
|
|
" <td id=\"T_61749_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_61749_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
|
|||
|
|
" <td id=\"T_61749_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_61749_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
|
|||
|
|
" <td id=\"T_61749_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_61749_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
|
|||
|
|
" <td id=\"T_61749_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_61749_level0_row4\" class=\"row_heading level0 row4\" >mlp</th>\n",
|
|||
|
|
" <td id=\"T_61749_row4_col0\" class=\"data row4 col0\" >0.996154</td>\n",
|
|||
|
|
" <td id=\"T_61749_row4_col1\" class=\"data row4 col1\" >0.991379</td>\n",
|
|||
|
|
" <td id=\"T_61749_row4_col2\" class=\"data row4 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row4_col3\" class=\"data row4 col3\" >0.988904</td>\n",
|
|||
|
|
" <td id=\"T_61749_row4_col4\" class=\"data row4 col4\" >0.988965</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_61749_level0_row5\" class=\"row_heading level0 row5\" >random_forest</th>\n",
|
|||
|
|
" <td id=\"T_61749_row5_col0\" class=\"data row5 col0\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row5_col2\" class=\"data row5 col2\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row5_col3\" class=\"data row5 col3\" >1.000000</td>\n",
|
|||
|
|
" <td id=\"T_61749_row5_col4\" class=\"data row5 col4\" >1.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_61749_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
|
|||
|
|
" <td id=\"T_61749_row6_col0\" class=\"data row6 col0\" >0.957692</td>\n",
|
|||
|
|
" <td id=\"T_61749_row6_col1\" class=\"data row6 col1\" >0.896226</td>\n",
|
|||
|
|
" <td id=\"T_61749_row6_col2\" class=\"data row6 col2\" >0.997858</td>\n",
|
|||
|
|
" <td id=\"T_61749_row6_col3\" class=\"data row6 col3\" >0.870015</td>\n",
|
|||
|
|
" <td id=\"T_61749_row6_col4\" class=\"data row6 col4\" >0.877459</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th id=\"T_61749_level0_row7\" class=\"row_heading level0 row7\" >naive_bayes</th>\n",
|
|||
|
|
" <td id=\"T_61749_row7_col0\" class=\"data row7 col0\" >0.978846</td>\n",
|
|||
|
|
" <td id=\"T_61749_row7_col1\" class=\"data row7 col1\" >0.954733</td>\n",
|
|||
|
|
" <td id=\"T_61749_row7_col2\" class=\"data row7 col2\" >0.983320</td>\n",
|
|||
|
|
" <td id=\"T_61749_row7_col3\" class=\"data row7 col3\" >0.940955</td>\n",
|
|||
|
|
" <td id=\"T_61749_row7_col4\" class=\"data row7 col4\" >0.942055</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
"<pandas.io.formats.style.Styler at 0x298e9ed6300>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"\n",
|
|||
|
|
"class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
|
|||
|
|
" [\n",
|
|||
|
|
" \"Accuracy_test\",\n",
|
|||
|
|
" \"F1_test\",\n",
|
|||
|
|
" \"ROC_AUC_test\",\n",
|
|||
|
|
" \"Cohen_kappa_test\",\n",
|
|||
|
|
" \"MCC_test\",\n",
|
|||
|
|
" ]\n",
|
|||
|
|
"]\n",
|
|||
|
|
"\n",
|
|||
|
|
"class_metrics = class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
"class_metrics.style.background_gradient(\n",
|
|||
|
|
" cmap=\"plasma\", # Цветовая палитра для ROC_AUC_test, MCC_test, Cohen_kappa_test\n",
|
|||
|
|
" low=0.3,\n",
|
|||
|
|
" high=1, \n",
|
|||
|
|
" subset=[\n",
|
|||
|
|
" \"ROC_AUC_test\",\n",
|
|||
|
|
" \"MCC_test\",\n",
|
|||
|
|
" \"Cohen_kappa_test\",\n",
|
|||
|
|
" ],\n",
|
|||
|
|
").background_gradient(\n",
|
|||
|
|
" cmap=\"viridis\", # Цветовая палитра для Accuracy_test, F1_test\n",
|
|||
|
|
" low=1, \n",
|
|||
|
|
" high=0.3, \n",
|
|||
|
|
" subset=[\n",
|
|||
|
|
" \"Accuracy_test\",\n",
|
|||
|
|
" \"F1_test\",\n",
|
|||
|
|
" ],\n",
|
|||
|
|
")\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"'logistic'"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"best_model = str(class_metrics.sort_values(by=\"MCC_test\", ascending=False).iloc[0].name)\n",
|
|||
|
|
"\n",
|
|||
|
|
"display(best_model)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Вывод данных с ошибкой предсказания для оценки"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"'Error items count: 0'"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Rank</th>\n",
|
|||
|
|
" <th>Predicted</th>\n",
|
|||
|
|
" <th>Name</th>\n",
|
|||
|
|
" <th>Networth</th>\n",
|
|||
|
|
" <th>Age</th>\n",
|
|||
|
|
" <th>Country</th>\n",
|
|||
|
|
" <th>Source</th>\n",
|
|||
|
|
" <th>Industry</th>\n",
|
|||
|
|
" <th>above_average_networth</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
"Empty DataFrame\n",
|
|||
|
|
"Columns: [Rank, Predicted, Name, Networth, Age, Country, Source, Industry, above_average_networth]\n",
|
|||
|
|
"Index: []"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"preprocessing_result = pipeline_end.transform(X_test)\n",
|
|||
|
|
"preprocessed_df = pd.DataFrame(\n",
|
|||
|
|
" preprocessing_result,\n",
|
|||
|
|
" columns=pipeline_end.get_feature_names_out(),\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"y_pred = class_models[best_model][\"preds\"]\n",
|
|||
|
|
"\n",
|
|||
|
|
"error_index = y_test[y_test[\"above_average_networth\"] != y_pred].index.tolist() \n",
|
|||
|
|
"display(f\"Error items count: {len(error_index)}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"error_predicted = pd.Series(y_pred, index=y_test.index).loc[error_index]\n",
|
|||
|
|
"error_df = X_test.loc[error_index].copy()\n",
|
|||
|
|
"error_df.insert(loc=1, column=\"Predicted\", value=error_predicted)\n",
|
|||
|
|
"error_df = error_df.sort_index() \n",
|
|||
|
|
"\n",
|
|||
|
|
"display(error_df)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Rank</th>\n",
|
|||
|
|
" <th>Name</th>\n",
|
|||
|
|
" <th>Networth</th>\n",
|
|||
|
|
" <th>Age</th>\n",
|
|||
|
|
" <th>Country</th>\n",
|
|||
|
|
" <th>Source</th>\n",
|
|||
|
|
" <th>Industry</th>\n",
|
|||
|
|
" <th>above_average_networth</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>231</th>\n",
|
|||
|
|
" <td>230</td>\n",
|
|||
|
|
" <td>Xavier Niel</td>\n",
|
|||
|
|
" <td>8.9</td>\n",
|
|||
|
|
" <td>54</td>\n",
|
|||
|
|
" <td>France</td>\n",
|
|||
|
|
" <td>internet, telecom</td>\n",
|
|||
|
|
" <td>Telecom</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Rank Name Networth Age Country Source Industry \\\n",
|
|||
|
|
"231 230 Xavier Niel 8.9 54 France internet, telecom Telecom \n",
|
|||
|
|
"\n",
|
|||
|
|
" above_average_networth \n",
|
|||
|
|
"231 1 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Networth</th>\n",
|
|||
|
|
" <th>Age</th>\n",
|
|||
|
|
" <th>Country_Argentina</th>\n",
|
|||
|
|
" <th>Country_Australia</th>\n",
|
|||
|
|
" <th>Country_Austria</th>\n",
|
|||
|
|
" <th>Country_Barbados</th>\n",
|
|||
|
|
" <th>Country_Belgium</th>\n",
|
|||
|
|
" <th>Country_Belize</th>\n",
|
|||
|
|
" <th>Country_Brazil</th>\n",
|
|||
|
|
" <th>Country_Bulgaria</th>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <th>Industry_Manufacturing</th>\n",
|
|||
|
|
" <th>Industry_Media & Entertainment</th>\n",
|
|||
|
|
" <th>Industry_Metals & Mining</th>\n",
|
|||
|
|
" <th>Industry_Real Estate</th>\n",
|
|||
|
|
" <th>Industry_Service</th>\n",
|
|||
|
|
" <th>Industry_Sports</th>\n",
|
|||
|
|
" <th>Industry_Technology</th>\n",
|
|||
|
|
" <th>Industry_Telecom</th>\n",
|
|||
|
|
" <th>Industry_diversified</th>\n",
|
|||
|
|
" <th>above_average_networth</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>231</th>\n",
|
|||
|
|
" <td>0.405144</td>\n",
|
|||
|
|
" <td>-0.782365</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>1 rows × 859 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Networth Age Country_Argentina Country_Australia \\\n",
|
|||
|
|
"231 0.405144 -0.782365 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Country_Austria Country_Barbados Country_Belgium Country_Belize \\\n",
|
|||
|
|
"231 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Country_Brazil Country_Bulgaria ... Industry_Manufacturing \\\n",
|
|||
|
|
"231 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Industry_Media & Entertainment Industry_Metals & Mining \\\n",
|
|||
|
|
"231 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Industry_Real Estate Industry_Service Industry_Sports \\\n",
|
|||
|
|
"231 0.0 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Industry_Technology Industry_Telecom Industry_diversified \\\n",
|
|||
|
|
"231 0.0 1.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" above_average_networth \n",
|
|||
|
|
"231 1.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[1 rows x 859 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"predicted: 1 (proba: [0.01305711 0.98694289])\n",
|
|||
|
|
"real: 1\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"model = class_models[best_model][\"pipeline\"]\n",
|
|||
|
|
"\n",
|
|||
|
|
"example_id = 253\n",
|
|||
|
|
"test = pd.DataFrame(X_test.iloc[example_id, :]).T\n",
|
|||
|
|
"display(test)\n",
|
|||
|
|
"test_preprocessed = pd.DataFrame(preprocessed_df.iloc[example_id, :]).T\n",
|
|||
|
|
"display(test_preprocessed)\n",
|
|||
|
|
"result_proba = model.predict_proba(test)[0]\n",
|
|||
|
|
"result = model.predict(test)[0]\n",
|
|||
|
|
"real = int(y_test.iloc[example_id].values[0])\n",
|
|||
|
|
"print(f\"predicted: {result} (proba: {result_proba})\")\n",
|
|||
|
|
"print(f\"real: {real}\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Задача регрессии - прогнозирование состояния миллионеров"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Среднее значение поля 'Networth': 4.8607499999999995\n",
|
|||
|
|
" Rank Name Networth Age Country \\\n",
|
|||
|
|
"0 1 Elon Musk 219.0 50 United States \n",
|
|||
|
|
"1 2 Jeff Bezos 171.0 58 United States \n",
|
|||
|
|
"2 3 Bernard Arnault & family 158.0 73 France \n",
|
|||
|
|
"3 4 Bill Gates 129.0 66 United States \n",
|
|||
|
|
"4 5 Warren Buffett 118.0 91 United States \n",
|
|||
|
|
"\n",
|
|||
|
|
" Source Industry above_average_networth \n",
|
|||
|
|
"0 Tesla, SpaceX Automotive 1 \n",
|
|||
|
|
"1 Amazon Technology 1 \n",
|
|||
|
|
"2 LVMH Fashion & Retail 1 \n",
|
|||
|
|
"3 Microsoft Technology 1 \n",
|
|||
|
|
"4 Berkshire Hathaway Finance & Investments 1 \n",
|
|||
|
|
"Статистическое описание DataFrame:\n",
|
|||
|
|
" Rank Networth Age above_average_networth\n",
|
|||
|
|
"count 2600.000000 2600.000000 2600.000000 2600.000000\n",
|
|||
|
|
"mean 1269.570769 4.860750 64.271923 0.225000\n",
|
|||
|
|
"std 728.146364 10.659671 13.220607 0.417663\n",
|
|||
|
|
"min 1.000000 1.000000 19.000000 0.000000\n",
|
|||
|
|
"25% 637.000000 1.500000 55.000000 0.000000\n",
|
|||
|
|
"50% 1292.000000 2.400000 64.000000 0.000000\n",
|
|||
|
|
"75% 1929.000000 4.500000 74.000000 0.000000\n",
|
|||
|
|
"max 2578.000000 219.000000 100.000000 1.000000\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"from sklearn import set_config\n",
|
|||
|
|
"\n",
|
|||
|
|
"set_config(transform_output=\"pandas\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"df = pd.read_csv(\"..//static//csv//Forbes Billionaires.csv\")\n",
|
|||
|
|
"random_state = 42\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Вычисление среднего значения поля \"Networth\"\n",
|
|||
|
|
"average_networth = df['Networth'].mean()\n",
|
|||
|
|
"print(f\"Среднее значение поля 'Networth': {average_networth}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание новой колонки, указывающей, выше или ниже среднего значение чистого состояния\n",
|
|||
|
|
"df['above_average_networth'] = (df['Networth'] > average_networth).astype(int)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(df.head())\n",
|
|||
|
|
"print(\"Статистическое описание DataFrame:\")\n",
|
|||
|
|
"print(df.describe())\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Разделение набора данных на обучающую и тестовые выборки (80/20) для задачи регрессии\n",
|
|||
|
|
"\n",
|
|||
|
|
"Целевой признак -- above_average_networth"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"'X_train'"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Rank</th>\n",
|
|||
|
|
" <th>Name</th>\n",
|
|||
|
|
" <th>Networth</th>\n",
|
|||
|
|
" <th>Age</th>\n",
|
|||
|
|
" <th>Country</th>\n",
|
|||
|
|
" <th>Source</th>\n",
|
|||
|
|
" <th>Industry</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>582</th>\n",
|
|||
|
|
" <td>579</td>\n",
|
|||
|
|
" <td>Alexandra Schoerghuber & family</td>\n",
|
|||
|
|
" <td>4.9</td>\n",
|
|||
|
|
" <td>63</td>\n",
|
|||
|
|
" <td>Germany</td>\n",
|
|||
|
|
" <td>real estate</td>\n",
|
|||
|
|
" <td>Real Estate</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>48</th>\n",
|
|||
|
|
" <td>49</td>\n",
|
|||
|
|
" <td>He Xiangjian</td>\n",
|
|||
|
|
" <td>28.3</td>\n",
|
|||
|
|
" <td>79</td>\n",
|
|||
|
|
" <td>China</td>\n",
|
|||
|
|
" <td>home appliances</td>\n",
|
|||
|
|
" <td>Manufacturing</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1772</th>\n",
|
|||
|
|
" <td>1729</td>\n",
|
|||
|
|
" <td>Bruce Mathieson</td>\n",
|
|||
|
|
" <td>1.7</td>\n",
|
|||
|
|
" <td>78</td>\n",
|
|||
|
|
" <td>Australia</td>\n",
|
|||
|
|
" <td>hotels</td>\n",
|
|||
|
|
" <td>Food & Beverage</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>964</th>\n",
|
|||
|
|
" <td>951</td>\n",
|
|||
|
|
" <td>Pansy Ho</td>\n",
|
|||
|
|
" <td>3.2</td>\n",
|
|||
|
|
" <td>59</td>\n",
|
|||
|
|
" <td>Hong Kong</td>\n",
|
|||
|
|
" <td>casinos</td>\n",
|
|||
|
|
" <td>Gambling & Casinos</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2213</th>\n",
|
|||
|
|
" <td>2190</td>\n",
|
|||
|
|
" <td>Sasson Dayan & family</td>\n",
|
|||
|
|
" <td>1.3</td>\n",
|
|||
|
|
" <td>82</td>\n",
|
|||
|
|
" <td>Brazil</td>\n",
|
|||
|
|
" <td>banking</td>\n",
|
|||
|
|
" <td>Finance & Investments</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1638</th>\n",
|
|||
|
|
" <td>1579</td>\n",
|
|||
|
|
" <td>Wang Chou-hsiong</td>\n",
|
|||
|
|
" <td>1.9</td>\n",
|
|||
|
|
" <td>81</td>\n",
|
|||
|
|
" <td>Taiwan</td>\n",
|
|||
|
|
" <td>footwear</td>\n",
|
|||
|
|
" <td>Manufacturing</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1095</th>\n",
|
|||
|
|
" <td>1096</td>\n",
|
|||
|
|
" <td>Jose Joao Abdalla Filho</td>\n",
|
|||
|
|
" <td>2.8</td>\n",
|
|||
|
|
" <td>76</td>\n",
|
|||
|
|
" <td>Brazil</td>\n",
|
|||
|
|
" <td>investments</td>\n",
|
|||
|
|
" <td>Finance & Investments</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1130</th>\n",
|
|||
|
|
" <td>1096</td>\n",
|
|||
|
|
" <td>Lin Chen-hai</td>\n",
|
|||
|
|
" <td>2.8</td>\n",
|
|||
|
|
" <td>75</td>\n",
|
|||
|
|
" <td>Taiwan</td>\n",
|
|||
|
|
" <td>real estate</td>\n",
|
|||
|
|
" <td>Real Estate</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1294</th>\n",
|
|||
|
|
" <td>1292</td>\n",
|
|||
|
|
" <td>Banwari Lal Bawri</td>\n",
|
|||
|
|
" <td>2.4</td>\n",
|
|||
|
|
" <td>69</td>\n",
|
|||
|
|
" <td>India</td>\n",
|
|||
|
|
" <td>pharmaceuticals</td>\n",
|
|||
|
|
" <td>Healthcare</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>860</th>\n",
|
|||
|
|
" <td>851</td>\n",
|
|||
|
|
" <td>Kuok Khoon Hong</td>\n",
|
|||
|
|
" <td>3.5</td>\n",
|
|||
|
|
" <td>72</td>\n",
|
|||
|
|
" <td>Singapore</td>\n",
|
|||
|
|
" <td>palm oil</td>\n",
|
|||
|
|
" <td>Manufacturing</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>2080 rows × 7 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Rank Name Networth Age Country \\\n",
|
|||
|
|
"582 579 Alexandra Schoerghuber & family 4.9 63 Germany \n",
|
|||
|
|
"48 49 He Xiangjian 28.3 79 China \n",
|
|||
|
|
"1772 1729 Bruce Mathieson 1.7 78 Australia \n",
|
|||
|
|
"964 951 Pansy Ho 3.2 59 Hong Kong \n",
|
|||
|
|
"2213 2190 Sasson Dayan & family 1.3 82 Brazil \n",
|
|||
|
|
"... ... ... ... ... ... \n",
|
|||
|
|
"1638 1579 Wang Chou-hsiong 1.9 81 Taiwan \n",
|
|||
|
|
"1095 1096 Jose Joao Abdalla Filho 2.8 76 Brazil \n",
|
|||
|
|
"1130 1096 Lin Chen-hai 2.8 75 Taiwan \n",
|
|||
|
|
"1294 1292 Banwari Lal Bawri 2.4 69 India \n",
|
|||
|
|
"860 851 Kuok Khoon Hong 3.5 72 Singapore \n",
|
|||
|
|
"\n",
|
|||
|
|
" Source Industry \n",
|
|||
|
|
"582 real estate Real Estate \n",
|
|||
|
|
"48 home appliances Manufacturing \n",
|
|||
|
|
"1772 hotels Food & Beverage \n",
|
|||
|
|
"964 casinos Gambling & Casinos \n",
|
|||
|
|
"2213 banking Finance & Investments \n",
|
|||
|
|
"... ... ... \n",
|
|||
|
|
"1638 footwear Manufacturing \n",
|
|||
|
|
"1095 investments Finance & Investments \n",
|
|||
|
|
"1130 real estate Real Estate \n",
|
|||
|
|
"1294 pharmaceuticals Healthcare \n",
|
|||
|
|
"860 palm oil Manufacturing \n",
|
|||
|
|
"\n",
|
|||
|
|
"[2080 rows x 7 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
"data": {
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
"metadata": {},
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
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|
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|||
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|
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|
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|
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|
|||
|
|
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|
|||
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|||
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|
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|
|||
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|
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|
|||
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|
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|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>above_average_networth</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>582</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>48</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1772</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>964</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2213</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1638</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1095</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1130</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1294</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>860</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>2080 rows × 1 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
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|
|||
|
|
" above_average_networth\n",
|
|||
|
|
"582 1\n",
|
|||
|
|
"48 1\n",
|
|||
|
|
"1772 0\n",
|
|||
|
|
"964 0\n",
|
|||
|
|
"2213 0\n",
|
|||
|
|
"... ...\n",
|
|||
|
|
"1638 0\n",
|
|||
|
|
"1095 0\n",
|
|||
|
|
"1130 0\n",
|
|||
|
|
"1294 0\n",
|
|||
|
|
"860 0\n",
|
|||
|
|
"\n",
|
|||
|
|
"[2080 rows x 1 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
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|
|||
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|
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|
|||
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|
|||
|
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|
"metadata": {},
|
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|
|
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|
|||
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},
|
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|
|||
|
|
"data": {
|
|||
|
|
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|||
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|||
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|||
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|
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|
|||
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|
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|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Rank</th>\n",
|
|||
|
|
" <th>Name</th>\n",
|
|||
|
|
" <th>Networth</th>\n",
|
|||
|
|
" <th>Age</th>\n",
|
|||
|
|
" <th>Country</th>\n",
|
|||
|
|
" <th>Source</th>\n",
|
|||
|
|
" <th>Industry</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1593</th>\n",
|
|||
|
|
" <td>1579</td>\n",
|
|||
|
|
" <td>Guangming Fu & family</td>\n",
|
|||
|
|
" <td>1.9</td>\n",
|
|||
|
|
" <td>68</td>\n",
|
|||
|
|
" <td>China</td>\n",
|
|||
|
|
" <td>poultry</td>\n",
|
|||
|
|
" <td>Food & Beverage</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>196</th>\n",
|
|||
|
|
" <td>197</td>\n",
|
|||
|
|
" <td>Leon Black</td>\n",
|
|||
|
|
" <td>10.0</td>\n",
|
|||
|
|
" <td>70</td>\n",
|
|||
|
|
" <td>United States</td>\n",
|
|||
|
|
" <td>private equity</td>\n",
|
|||
|
|
" <td>Finance & Investments</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>239</th>\n",
|
|||
|
|
" <td>235</td>\n",
|
|||
|
|
" <td>Zong Qinghou</td>\n",
|
|||
|
|
" <td>8.8</td>\n",
|
|||
|
|
" <td>76</td>\n",
|
|||
|
|
" <td>China</td>\n",
|
|||
|
|
" <td>beverages</td>\n",
|
|||
|
|
" <td>Food & Beverage</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2126</th>\n",
|
|||
|
|
" <td>2076</td>\n",
|
|||
|
|
" <td>Kurt Krieger</td>\n",
|
|||
|
|
" <td>1.4</td>\n",
|
|||
|
|
" <td>74</td>\n",
|
|||
|
|
" <td>Germany</td>\n",
|
|||
|
|
" <td>furniture retailing</td>\n",
|
|||
|
|
" <td>Fashion & Retail</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1587</th>\n",
|
|||
|
|
" <td>1579</td>\n",
|
|||
|
|
" <td>Chen Kaichen</td>\n",
|
|||
|
|
" <td>1.9</td>\n",
|
|||
|
|
" <td>64</td>\n",
|
|||
|
|
" <td>China</td>\n",
|
|||
|
|
" <td>household chemicals</td>\n",
|
|||
|
|
" <td>Manufacturing</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1778</th>\n",
|
|||
|
|
" <td>1729</td>\n",
|
|||
|
|
" <td>Jorge Perez</td>\n",
|
|||
|
|
" <td>1.7</td>\n",
|
|||
|
|
" <td>72</td>\n",
|
|||
|
|
" <td>United States</td>\n",
|
|||
|
|
" <td>real estate</td>\n",
|
|||
|
|
" <td>Real Estate</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>166</th>\n",
|
|||
|
|
" <td>167</td>\n",
|
|||
|
|
" <td>Brian Chesky</td>\n",
|
|||
|
|
" <td>11.5</td>\n",
|
|||
|
|
" <td>40</td>\n",
|
|||
|
|
" <td>United States</td>\n",
|
|||
|
|
" <td>Airbnb</td>\n",
|
|||
|
|
" <td>Technology</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>949</th>\n",
|
|||
|
|
" <td>913</td>\n",
|
|||
|
|
" <td>Zhong Ruonong & family</td>\n",
|
|||
|
|
" <td>3.3</td>\n",
|
|||
|
|
" <td>59</td>\n",
|
|||
|
|
" <td>China</td>\n",
|
|||
|
|
" <td>electronics</td>\n",
|
|||
|
|
" <td>Manufacturing</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>49</th>\n",
|
|||
|
|
" <td>50</td>\n",
|
|||
|
|
" <td>Miriam Adelson</td>\n",
|
|||
|
|
" <td>27.5</td>\n",
|
|||
|
|
" <td>76</td>\n",
|
|||
|
|
" <td>United States</td>\n",
|
|||
|
|
" <td>casinos</td>\n",
|
|||
|
|
" <td>Gambling & Casinos</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2511</th>\n",
|
|||
|
|
" <td>2448</td>\n",
|
|||
|
|
" <td>Lou Boliang</td>\n",
|
|||
|
|
" <td>1.1</td>\n",
|
|||
|
|
" <td>58</td>\n",
|
|||
|
|
" <td>United States</td>\n",
|
|||
|
|
" <td>pharmaceuticals</td>\n",
|
|||
|
|
" <td>Healthcare</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>520 rows × 7 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Rank Name Networth Age Country \\\n",
|
|||
|
|
"1593 1579 Guangming Fu & family 1.9 68 China \n",
|
|||
|
|
"196 197 Leon Black 10.0 70 United States \n",
|
|||
|
|
"239 235 Zong Qinghou 8.8 76 China \n",
|
|||
|
|
"2126 2076 Kurt Krieger 1.4 74 Germany \n",
|
|||
|
|
"1587 1579 Chen Kaichen 1.9 64 China \n",
|
|||
|
|
"... ... ... ... ... ... \n",
|
|||
|
|
"1778 1729 Jorge Perez 1.7 72 United States \n",
|
|||
|
|
"166 167 Brian Chesky 11.5 40 United States \n",
|
|||
|
|
"949 913 Zhong Ruonong & family 3.3 59 China \n",
|
|||
|
|
"49 50 Miriam Adelson 27.5 76 United States \n",
|
|||
|
|
"2511 2448 Lou Boliang 1.1 58 United States \n",
|
|||
|
|
"\n",
|
|||
|
|
" Source Industry \n",
|
|||
|
|
"1593 poultry Food & Beverage \n",
|
|||
|
|
"196 private equity Finance & Investments \n",
|
|||
|
|
"239 beverages Food & Beverage \n",
|
|||
|
|
"2126 furniture retailing Fashion & Retail \n",
|
|||
|
|
"1587 household chemicals Manufacturing \n",
|
|||
|
|
"... ... ... \n",
|
|||
|
|
"1778 real estate Real Estate \n",
|
|||
|
|
"166 Airbnb Technology \n",
|
|||
|
|
"949 electronics Manufacturing \n",
|
|||
|
|
"49 casinos Gambling & Casinos \n",
|
|||
|
|
"2511 pharmaceuticals Healthcare \n",
|
|||
|
|
"\n",
|
|||
|
|
"[520 rows x 7 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
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|
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|
|
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|
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|
|
"data": {
|
|||
|
|
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|
|||
|
|
"'y_test'"
|
|||
|
|
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|
|||
|
|
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|
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|
|
"metadata": {},
|
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|
|
"output_type": "display_data"
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>above_average_networth</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1593</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>196</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>239</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2126</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1587</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1778</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>166</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>949</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>49</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2511</th>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>520 rows × 1 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" above_average_networth\n",
|
|||
|
|
"1593 0\n",
|
|||
|
|
"196 1\n",
|
|||
|
|
"239 1\n",
|
|||
|
|
"2126 0\n",
|
|||
|
|
"1587 0\n",
|
|||
|
|
"... ...\n",
|
|||
|
|
"1778 0\n",
|
|||
|
|
"166 1\n",
|
|||
|
|
"949 0\n",
|
|||
|
|
"49 1\n",
|
|||
|
|
"2511 0\n",
|
|||
|
|
"\n",
|
|||
|
|
"[520 rows x 1 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from typing import Tuple\n",
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"from pandas import DataFrame\n",
|
|||
|
|
"from sklearn.model_selection import train_test_split\n",
|
|||
|
|
"\n",
|
|||
|
|
"def split_into_train_test(\n",
|
|||
|
|
" df_input: DataFrame,\n",
|
|||
|
|
" target_colname: str = \"above_average_networth\", \n",
|
|||
|
|
" frac_train: float = 0.8,\n",
|
|||
|
|
" random_state: int = None,\n",
|
|||
|
|
") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame]:\n",
|
|||
|
|
" \n",
|
|||
|
|
" if not (0 < frac_train < 1):\n",
|
|||
|
|
" raise ValueError(\"Fraction must be between 0 and 1.\")\n",
|
|||
|
|
" if target_colname not in df_input.columns:\n",
|
|||
|
|
" raise ValueError(f\"{target_colname} is not a column in the DataFrame.\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" X = df_input.drop(columns=[target_colname]) \n",
|
|||
|
|
" y = df_input[[target_colname]] \n",
|
|||
|
|
"\n",
|
|||
|
|
" X_train, X_test, y_train, y_test = train_test_split(\n",
|
|||
|
|
" X, y,\n",
|
|||
|
|
" test_size=(1.0 - frac_train),\n",
|
|||
|
|
" random_state=random_state\n",
|
|||
|
|
" )\n",
|
|||
|
|
" \n",
|
|||
|
|
" return X_train, X_test, y_train, y_test\n",
|
|||
|
|
"\n",
|
|||
|
|
"X_train, X_test, y_train, y_test = split_into_train_test(\n",
|
|||
|
|
" df, \n",
|
|||
|
|
" target_colname=\"above_average_networth\", \n",
|
|||
|
|
" frac_train=0.8, \n",
|
|||
|
|
" random_state=42 \n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Отображение результатов\n",
|
|||
|
|
"display(\"X_train\", X_train)\n",
|
|||
|
|
"display(\"y_train\", y_train)\n",
|
|||
|
|
"\n",
|
|||
|
|
"display(\"X_test\", X_test)\n",
|
|||
|
|
"display(\"y_test\", y_test)\n",
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Формирование конвейера для решения задачи регрессии"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
" Networth Age Country_Argentina Country_Australia \\\n",
|
|||
|
|
"0 20.092595 -1.079729 0.0 0.0 \n",
|
|||
|
|
"1 15.588775 -0.474496 0.0 0.0 \n",
|
|||
|
|
"2 14.368991 0.660314 0.0 0.0 \n",
|
|||
|
|
"3 11.647933 0.130736 0.0 0.0 \n",
|
|||
|
|
"4 10.615808 2.022087 0.0 0.0 \n",
|
|||
|
|
"... ... ... ... ... \n",
|
|||
|
|
"2595 -0.362253 1.189893 0.0 0.0 \n",
|
|||
|
|
"2596 -0.362253 1.341201 0.0 0.0 \n",
|
|||
|
|
"2597 -0.362253 0.509006 0.0 0.0 \n",
|
|||
|
|
"2598 -0.362253 0.282044 0.0 0.0 \n",
|
|||
|
|
"2599 -0.362253 0.357698 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Country_Austria Country_Barbados Country_Belgium Country_Belize \\\n",
|
|||
|
|
"0 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"1 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"2 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"3 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"4 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"... ... ... ... ... \n",
|
|||
|
|
"2595 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"2596 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"2597 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"2598 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"2599 0.0 0.0 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Country_Brazil Country_Bulgaria ... Industry_Manufacturing \\\n",
|
|||
|
|
"0 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"1 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"2 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"3 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"4 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"... ... ... ... ... \n",
|
|||
|
|
"2595 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"2596 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"2597 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"2598 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"2599 0.0 0.0 ... 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Industry_Media & Entertainment Industry_Metals & Mining \\\n",
|
|||
|
|
"0 0.0 0.0 \n",
|
|||
|
|
"1 0.0 0.0 \n",
|
|||
|
|
"2 0.0 0.0 \n",
|
|||
|
|
"3 0.0 0.0 \n",
|
|||
|
|
"4 0.0 0.0 \n",
|
|||
|
|
"... ... ... \n",
|
|||
|
|
"2595 0.0 0.0 \n",
|
|||
|
|
"2596 0.0 0.0 \n",
|
|||
|
|
"2597 0.0 0.0 \n",
|
|||
|
|
"2598 0.0 0.0 \n",
|
|||
|
|
"2599 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Industry_Real Estate Industry_Service Industry_Sports \\\n",
|
|||
|
|
"0 0.0 0.0 0.0 \n",
|
|||
|
|
"1 0.0 0.0 0.0 \n",
|
|||
|
|
"2 0.0 0.0 0.0 \n",
|
|||
|
|
"3 0.0 0.0 0.0 \n",
|
|||
|
|
"4 0.0 0.0 0.0 \n",
|
|||
|
|
"... ... ... ... \n",
|
|||
|
|
"2595 0.0 0.0 0.0 \n",
|
|||
|
|
"2596 0.0 0.0 0.0 \n",
|
|||
|
|
"2597 0.0 0.0 0.0 \n",
|
|||
|
|
"2598 0.0 0.0 0.0 \n",
|
|||
|
|
"2599 0.0 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Industry_Technology Industry_Telecom Industry_diversified \\\n",
|
|||
|
|
"0 0.0 0.0 0.0 \n",
|
|||
|
|
"1 1.0 0.0 0.0 \n",
|
|||
|
|
"2 0.0 0.0 0.0 \n",
|
|||
|
|
"3 1.0 0.0 0.0 \n",
|
|||
|
|
"4 0.0 0.0 0.0 \n",
|
|||
|
|
"... ... ... ... \n",
|
|||
|
|
"2595 0.0 0.0 0.0 \n",
|
|||
|
|
"2596 0.0 0.0 0.0 \n",
|
|||
|
|
"2597 0.0 0.0 0.0 \n",
|
|||
|
|
"2598 0.0 0.0 0.0 \n",
|
|||
|
|
"2599 0.0 0.0 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Networth_per_Age \n",
|
|||
|
|
"0 -18.608929 \n",
|
|||
|
|
"1 -32.853309 \n",
|
|||
|
|
"2 21.760834 \n",
|
|||
|
|
"3 89.095063 \n",
|
|||
|
|
"4 5.249926 \n",
|
|||
|
|
"... ... \n",
|
|||
|
|
"2595 -0.304441 \n",
|
|||
|
|
"2596 -0.270096 \n",
|
|||
|
|
"2597 -0.711686 \n",
|
|||
|
|
"2598 -1.284383 \n",
|
|||
|
|
"2599 -1.012732 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[2600 rows x 988 columns]\n",
|
|||
|
|
"(2600, 988)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"from sklearn.base import BaseEstimator, TransformerMixin\n",
|
|||
|
|
"from sklearn.compose import ColumnTransformer\n",
|
|||
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|||
|
|
"from sklearn.impute import SimpleImputer\n",
|
|||
|
|
"from sklearn.pipeline import Pipeline\n",
|
|||
|
|
"from sklearn.preprocessing import OneHotEncoder\n",
|
|||
|
|
"from sklearn.ensemble import RandomForestRegressor \n",
|
|||
|
|
"from sklearn.model_selection import train_test_split\n",
|
|||
|
|
"from sklearn.pipeline import make_pipeline\n",
|
|||
|
|
"\n",
|
|||
|
|
"class ForbesBillionairesFeatures(BaseEstimator, TransformerMixin): \n",
|
|||
|
|
" def __init__(self):\n",
|
|||
|
|
" pass\n",
|
|||
|
|
"\n",
|
|||
|
|
" def fit(self, X, y=None):\n",
|
|||
|
|
" return self\n",
|
|||
|
|
"\n",
|
|||
|
|
" def transform(self, X, y=None):\n",
|
|||
|
|
" X[\"Networth_per_Age\"] = X[\"Networth\"] / X[\"Age\"]\n",
|
|||
|
|
" return X\n",
|
|||
|
|
"\n",
|
|||
|
|
" def get_feature_names_out(self, features_in):\n",
|
|||
|
|
" return np.append(features_in, [\"Networth_per_Age\"], axis=0) \n",
|
|||
|
|
"\n",
|
|||
|
|
"columns_to_drop = [\"Rank\", \"Name\"] \n",
|
|||
|
|
"num_columns = [\"Networth\", \"Age\"] \n",
|
|||
|
|
"cat_columns = [\"Country\", \"Source\", \"Industry\"]\n",
|
|||
|
|
"\n",
|
|||
|
|
"num_imputer = SimpleImputer(strategy=\"median\")\n",
|
|||
|
|
"num_scaler = StandardScaler()\n",
|
|||
|
|
"preprocessing_num = Pipeline(\n",
|
|||
|
|
" [\n",
|
|||
|
|
" (\"imputer\", num_imputer),\n",
|
|||
|
|
" (\"scaler\", num_scaler),\n",
|
|||
|
|
" ]\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
|
|||
|
|
"cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
|
|||
|
|
"preprocessing_cat = Pipeline(\n",
|
|||
|
|
" [\n",
|
|||
|
|
" (\"imputer\", cat_imputer),\n",
|
|||
|
|
" (\"encoder\", cat_encoder),\n",
|
|||
|
|
" ]\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"features_preprocessing = ColumnTransformer(\n",
|
|||
|
|
" verbose_feature_names_out=False,\n",
|
|||
|
|
" transformers=[\n",
|
|||
|
|
" (\"prepocessing_num\", preprocessing_num, num_columns),\n",
|
|||
|
|
" (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
|
|||
|
|
" ],\n",
|
|||
|
|
" remainder=\"passthrough\" \n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"drop_columns = ColumnTransformer(\n",
|
|||
|
|
" verbose_feature_names_out=False,\n",
|
|||
|
|
" transformers=[\n",
|
|||
|
|
" (\"drop_columns\", \"drop\", columns_to_drop),\n",
|
|||
|
|
" ],\n",
|
|||
|
|
" remainder=\"passthrough\",\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"pipeline_end = Pipeline(\n",
|
|||
|
|
" [\n",
|
|||
|
|
" (\"features_preprocessing\", features_preprocessing),\n",
|
|||
|
|
" (\"drop_columns\", drop_columns),\n",
|
|||
|
|
" (\"custom_features\", ForbesBillionairesFeatures()), # Добавляем custom_features\n",
|
|||
|
|
" ]\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"df = pd.read_csv(\"..//static//csv//Forbes Billionaires.csv\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"average_networth = df['Networth'].mean()\n",
|
|||
|
|
"df['above_average_networth'] = (df['Networth'] > average_networth).astype(int)\n",
|
|||
|
|
"\n",
|
|||
|
|
"X = df.drop('above_average_networth', axis=1)\n",
|
|||
|
|
"y = df['above_average_networth'].values.ravel()\n",
|
|||
|
|
"\n",
|
|||
|
|
"X_processed = pipeline_end.fit_transform(X)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(X_processed)\n",
|
|||
|
|
"print(X_processed.shape)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Формирование набора моделей для регрессии"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 41,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
|||
|
|
" return fit_method(estimator, *args, **kwargs)\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
|||
|
|
" return fit_method(estimator, *args, **kwargs)\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
|||
|
|
" return fit_method(estimator, *args, **kwargs)\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
|||
|
|
" return fit_method(estimator, *args, **kwargs)\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
|||
|
|
" return fit_method(estimator, *args, **kwargs)\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|||
|
|
" y = column_or_1d(y, warn=True) # TODO: Is this still required?\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|||
|
|
" y = column_or_1d(y, warn=True) # TODO: Is this still required?\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|||
|
|
" y = column_or_1d(y, warn=True) # TODO: Is this still required?\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|||
|
|
" y = column_or_1d(y, warn=True) # TODO: Is this still required?\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|||
|
|
" y = column_or_1d(y, warn=True) # TODO: Is this still required?\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|||
|
|
" y = column_or_1d(y, warn=True)\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|||
|
|
" y = column_or_1d(y, warn=True)\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|||
|
|
" y = column_or_1d(y, warn=True)\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|||
|
|
" y = column_or_1d(y, warn=True)\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|||
|
|
" y = column_or_1d(y, warn=True)\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Random Forest: Mean Score = 0.9999853281013186, Standard Deviation = 2.6771556649877537e-05\n",
|
|||
|
|
"Linear Regression: Mean Score = -2.4184625766523136e+22, Standard Deviation = 2.867931120379175e+22\n",
|
|||
|
|
"Gradient Boosting: Mean Score = 0.9999999992916644, Standard Deviation = 2.7301021406313204e-12\n",
|
|||
|
|
"Support Vector Regression: Mean Score = 0.6826855358064325, Standard Deviation = 0.02039531518474594\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.linear_model import LinearRegression\n",
|
|||
|
|
"from sklearn.ensemble import GradientBoostingRegressor\n",
|
|||
|
|
"from sklearn.svm import SVR\n",
|
|||
|
|
"from sklearn.model_selection import cross_val_score\n",
|
|||
|
|
"\n",
|
|||
|
|
"def train_multiple_models(X, y, models):\n",
|
|||
|
|
" results = {}\n",
|
|||
|
|
" for model_name, model in models.items():\n",
|
|||
|
|
" model_pipeline = Pipeline(\n",
|
|||
|
|
" [\n",
|
|||
|
|
" (\"features_preprocessing\", features_preprocessing),\n",
|
|||
|
|
" (\"drop_columns\", drop_columns),\n",
|
|||
|
|
" (\"model\", model) \n",
|
|||
|
|
" ]\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" scores = cross_val_score(model_pipeline, X, y, cv=5) \n",
|
|||
|
|
" results[model_name] = {\n",
|
|||
|
|
" \"mean_score\": scores.mean(),\n",
|
|||
|
|
" \"std_dev\": scores.std()\n",
|
|||
|
|
" }\n",
|
|||
|
|
" \n",
|
|||
|
|
" return results\n",
|
|||
|
|
"\n",
|
|||
|
|
"models = {\n",
|
|||
|
|
" \"Random Forest\": RandomForestRegressor(),\n",
|
|||
|
|
" \"Linear Regression\": LinearRegression(),\n",
|
|||
|
|
" \"Gradient Boosting\": GradientBoostingRegressor(),\n",
|
|||
|
|
" \"Support Vector Regression\": SVR()\n",
|
|||
|
|
"}\n",
|
|||
|
|
"\n",
|
|||
|
|
"results = train_multiple_models(X_train, y_train, models)\n",
|
|||
|
|
"\n",
|
|||
|
|
"for model_name, scores in results.items():\n",
|
|||
|
|
" print(f\"{model_name}: Mean Score = {scores['mean_score']}, Standard Deviation = {scores['std_dev']}\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 42,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: logistic\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"MSE (train): 0.0125\n",
|
|||
|
|
"MSE (test): 0.04038461538461539\n",
|
|||
|
|
"MAE (train): 0.0125\n",
|
|||
|
|
"MAE (test): 0.04038461538461539\n",
|
|||
|
|
"R2 (train): 0.9275415718173158\n",
|
|||
|
|
"R2 (test): 0.7776148582600195\n",
|
|||
|
|
"STD (train): 0.11110243021644485\n",
|
|||
|
|
"STD (test): 0.19685959012669935\n",
|
|||
|
|
"----------------------------------------\n",
|
|||
|
|
"Model: ridge\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"MSE (train): 0.004326923076923077\n",
|
|||
|
|
"MSE (test): 0.013461538461538462\n",
|
|||
|
|
"MAE (train): 0.004326923076923077\n",
|
|||
|
|
"MAE (test): 0.013461538461538462\n",
|
|||
|
|
"R2 (train): 0.9749182363983017\n",
|
|||
|
|
"R2 (test): 0.9258716194200065\n",
|
|||
|
|
"STD (train): 0.0656368860749005\n",
|
|||
|
|
"STD (test): 0.11588034534756023\n",
|
|||
|
|
"----------------------------------------\n",
|
|||
|
|
"Model: decision_tree\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"MSE (train): 0.0\n",
|
|||
|
|
"MSE (test): 0.0\n",
|
|||
|
|
"MAE (train): 0.0\n",
|
|||
|
|
"MAE (test): 0.0\n",
|
|||
|
|
"R2 (train): 1.0\n",
|
|||
|
|
"R2 (test): 1.0\n",
|
|||
|
|
"STD (train): 0.0\n",
|
|||
|
|
"STD (test): 0.0\n",
|
|||
|
|
"----------------------------------------\n",
|
|||
|
|
"Model: knn\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"MSE (train): 0.09278846153846154\n",
|
|||
|
|
"MSE (test): 0.15384615384615385\n",
|
|||
|
|
"MAE (train): 0.09278846153846154\n",
|
|||
|
|
"MAE (test): 0.15384615384615385\n",
|
|||
|
|
"R2 (train): 0.4621355138746903\n",
|
|||
|
|
"R2 (test): 0.1528185076572175\n",
|
|||
|
|
"STD (train): 0.29276240884468824\n",
|
|||
|
|
"STD (test): 0.3684085396282311\n",
|
|||
|
|
"----------------------------------------\n",
|
|||
|
|
"Model: naive_bayes\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"MSE (train): 0.37740384615384615\n",
|
|||
|
|
"MSE (test): 0.6096153846153847\n",
|
|||
|
|
"MAE (train): 0.37740384615384615\n",
|
|||
|
|
"MAE (test): 0.6096153846153847\n",
|
|||
|
|
"R2 (train): -1.1876871585925808\n",
|
|||
|
|
"R2 (test): -2.3569566634082757\n",
|
|||
|
|
"STD (train): 0.4847372309428379\n",
|
|||
|
|
"STD (test): 0.5672229402142737\n",
|
|||
|
|
"----------------------------------------\n",
|
|||
|
|
"Model: gradient_boosting\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"MSE (train): 0.0\n",
|
|||
|
|
"MSE (test): 0.0\n",
|
|||
|
|
"MAE (train): 0.0\n",
|
|||
|
|
"MAE (test): 0.0\n",
|
|||
|
|
"R2 (train): 1.0\n",
|
|||
|
|
"R2 (test): 1.0\n",
|
|||
|
|
"STD (train): 0.0\n",
|
|||
|
|
"STD (test): 0.0\n",
|
|||
|
|
"----------------------------------------\n",
|
|||
|
|
"Model: random_forest\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"MSE (train): 0.0\n",
|
|||
|
|
"MSE (test): 0.0\n",
|
|||
|
|
"MAE (train): 0.0\n",
|
|||
|
|
"MAE (test): 0.0\n",
|
|||
|
|
"R2 (train): 1.0\n",
|
|||
|
|
"R2 (test): 1.0\n",
|
|||
|
|
"STD (train): 0.0\n",
|
|||
|
|
"STD (test): 0.0\n",
|
|||
|
|
"----------------------------------------\n",
|
|||
|
|
"Model: mlp\n",
|
|||
|
|
"MSE (train): 0.06778846153846153\n",
|
|||
|
|
"MSE (test): 0.12692307692307692\n",
|
|||
|
|
"MAE (train): 0.06778846153846153\n",
|
|||
|
|
"MAE (test): 0.12692307692307692\n",
|
|||
|
|
"R2 (train): 0.6070523702400588\n",
|
|||
|
|
"R2 (test): 0.30107526881720437\n",
|
|||
|
|
"STD (train): 0.2521427220700598\n",
|
|||
|
|
"STD (test): 0.3370600353877945\n",
|
|||
|
|
"----------------------------------------\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"from sklearn import metrics\n",
|
|||
|
|
"from sklearn.pipeline import Pipeline\n",
|
|||
|
|
"\n",
|
|||
|
|
"if 'class_models' not in locals():\n",
|
|||
|
|
" raise ValueError(\"class_models is not defined\")\n",
|
|||
|
|
"if 'X_train' not in locals() or 'X_test' not in locals() or 'y_train' not in locals() or 'y_test' not in locals():\n",
|
|||
|
|
" raise ValueError(\"Train/test data is not defined\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"y_train = np.ravel(y_train) \n",
|
|||
|
|
"y_test = np.ravel(y_test) \n",
|
|||
|
|
"\n",
|
|||
|
|
"results = []\n",
|
|||
|
|
"\n",
|
|||
|
|
"for model_name in class_models.keys():\n",
|
|||
|
|
" print(f\"Model: {model_name}\")\n",
|
|||
|
|
" \n",
|
|||
|
|
" model = class_models[model_name][\"model\"]\n",
|
|||
|
|
" \n",
|
|||
|
|
" model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
|
|||
|
|
" \n",
|
|||
|
|
" model_pipeline.fit(X_train, y_train)\n",
|
|||
|
|
"\n",
|
|||
|
|
" y_train_predict = model_pipeline.predict(X_train)\n",
|
|||
|
|
" y_test_predict = model_pipeline.predict(X_test)\n",
|
|||
|
|
"\n",
|
|||
|
|
" class_models[model_name][\"pipeline\"] = model_pipeline\n",
|
|||
|
|
" class_models[model_name][\"preds\"] = y_test_predict\n",
|
|||
|
|
"\n",
|
|||
|
|
" class_models[model_name][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_predict)\n",
|
|||
|
|
" class_models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_predict)\n",
|
|||
|
|
" class_models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_predict)\n",
|
|||
|
|
" class_models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_predict)\n",
|
|||
|
|
" class_models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_predict)\n",
|
|||
|
|
" class_models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_predict)\n",
|
|||
|
|
"\n",
|
|||
|
|
" class_models[model_name][\"STD_train\"] = np.std(y_train - y_train_predict)\n",
|
|||
|
|
" class_models[model_name][\"STD_test\"] = np.std(y_test - y_test_predict)\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(f\"MSE (train): {class_models[model_name]['MSE_train']}\")\n",
|
|||
|
|
" print(f\"MSE (test): {class_models[model_name]['MSE_test']}\")\n",
|
|||
|
|
" print(f\"MAE (train): {class_models[model_name]['MAE_train']}\")\n",
|
|||
|
|
" print(f\"MAE (test): {class_models[model_name]['MAE_test']}\")\n",
|
|||
|
|
" print(f\"R2 (train): {class_models[model_name]['R2_train']}\")\n",
|
|||
|
|
" print(f\"R2 (test): {class_models[model_name]['R2_test']}\")\n",
|
|||
|
|
" print(f\"STD (train): {class_models[model_name]['STD_train']}\")\n",
|
|||
|
|
" print(f\"STD (test): {class_models[model_name]['STD_test']}\")\n",
|
|||
|
|
" print(\"-\" * 40) "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Пример использования обученной модели (конвейера регрессии) для предсказания"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 43,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: RandomForest\n",
|
|||
|
|
"MSE (train): 24.028673442957558\n",
|
|||
|
|
"MSE (test): 68.96006650623248\n",
|
|||
|
|
"MAE (train): 1.548185999451937\n",
|
|||
|
|
"MAE (test): 3.372747412240537\n",
|
|||
|
|
"R2 (train): 0.8231149198653249\n",
|
|||
|
|
"R2 (test): -1.9013866015383956\n",
|
|||
|
|
"----------------------------------------\n",
|
|||
|
|
"Прогнозируемое чистое состояние: 1.3689999999999998\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1, 2] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"from sklearn import metrics\n",
|
|||
|
|
"from sklearn.pipeline import Pipeline\n",
|
|||
|
|
"from sklearn.model_selection import train_test_split\n",
|
|||
|
|
"from sklearn.ensemble import RandomForestRegressor \n",
|
|||
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|||
|
|
"from sklearn.compose import ColumnTransformer\n",
|
|||
|
|
"from sklearn.impute import SimpleImputer\n",
|
|||
|
|
"from sklearn.preprocessing import OneHotEncoder\n",
|
|||
|
|
"\n",
|
|||
|
|
"data = pd.read_csv(\"..//static//csv//Forbes Billionaires.csv\") \n",
|
|||
|
|
"\n",
|
|||
|
|
"average_networth = data['Networth'].mean()\n",
|
|||
|
|
"data['above_average_networth'] = (data['Networth'] > average_networth).astype(int) \n",
|
|||
|
|
"\n",
|
|||
|
|
"X = data.drop('Networth', axis=1) \n",
|
|||
|
|
"y = data['Networth']\n",
|
|||
|
|
"\n",
|
|||
|
|
"class_models = {\n",
|
|||
|
|
" \"RandomForest\": {\n",
|
|||
|
|
" \"model\": RandomForestRegressor(n_estimators=100, random_state=42),\n",
|
|||
|
|
" }\n",
|
|||
|
|
"}\n",
|
|||
|
|
"\n",
|
|||
|
|
"num_columns = ['Age']\n",
|
|||
|
|
"cat_columns = ['Country', 'Source', 'Industry']\n",
|
|||
|
|
"\n",
|
|||
|
|
"num_transformer = Pipeline(steps=[\n",
|
|||
|
|
" ('imputer', SimpleImputer(strategy='median')),\n",
|
|||
|
|
" ('scaler', StandardScaler())\n",
|
|||
|
|
"])\n",
|
|||
|
|
"\n",
|
|||
|
|
"cat_transformer = Pipeline(steps=[\n",
|
|||
|
|
" ('imputer', SimpleImputer(strategy='constant', fill_value='unknown')),\n",
|
|||
|
|
" ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False, drop=\"first\"))\n",
|
|||
|
|
"])\n",
|
|||
|
|
"\n",
|
|||
|
|
"preprocessor = ColumnTransformer(\n",
|
|||
|
|
" transformers=[\n",
|
|||
|
|
" ('num', num_transformer, num_columns),\n",
|
|||
|
|
" ('cat', cat_transformer, cat_columns)\n",
|
|||
|
|
" ])\n",
|
|||
|
|
"\n",
|
|||
|
|
"pipeline_end = Pipeline(steps=[\n",
|
|||
|
|
" ('preprocessor', preprocessor),\n",
|
|||
|
|
"])\n",
|
|||
|
|
"\n",
|
|||
|
|
"results = []\n",
|
|||
|
|
"\n",
|
|||
|
|
"for model_name in class_models.keys():\n",
|
|||
|
|
" print(f\"Model: {model_name}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" model = class_models[model_name][\"model\"]\n",
|
|||
|
|
" model_pipeline = Pipeline(steps=[\n",
|
|||
|
|
" ('preprocessor', preprocessor),\n",
|
|||
|
|
" ('model', model)\n",
|
|||
|
|
" ])\n",
|
|||
|
|
"\n",
|
|||
|
|
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
|
|||
|
|
"\n",
|
|||
|
|
" model_pipeline.fit(X_train, y_train)\n",
|
|||
|
|
"\n",
|
|||
|
|
" y_train_predict = model_pipeline.predict(X_train)\n",
|
|||
|
|
" y_test_predict = model_pipeline.predict(X_test)\n",
|
|||
|
|
"\n",
|
|||
|
|
" class_models[model_name][\"preds\"] = y_test_predict\n",
|
|||
|
|
"\n",
|
|||
|
|
" class_models[model_name][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_predict)\n",
|
|||
|
|
" class_models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_predict)\n",
|
|||
|
|
" class_models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_predict)\n",
|
|||
|
|
" class_models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_predict)\n",
|
|||
|
|
" class_models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_predict)\n",
|
|||
|
|
" class_models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_predict)\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(f\"MSE (train): {class_models[model_name]['MSE_train']}\")\n",
|
|||
|
|
" print(f\"MSE (test): {class_models[model_name]['MSE_test']}\")\n",
|
|||
|
|
" print(f\"MAE (train): {class_models[model_name]['MAE_train']}\")\n",
|
|||
|
|
" print(f\"MAE (test): {class_models[model_name]['MAE_test']}\")\n",
|
|||
|
|
" print(f\"R2 (train): {class_models[model_name]['R2_train']}\")\n",
|
|||
|
|
" print(f\"R2 (test): {class_models[model_name]['R2_test']}\")\n",
|
|||
|
|
" print(\"-\" * 40)\n",
|
|||
|
|
"\n",
|
|||
|
|
"new_billionaire_data = pd.DataFrame({\n",
|
|||
|
|
" 'Age': [50],\n",
|
|||
|
|
" 'Country': ['USA'],\n",
|
|||
|
|
" 'Source': ['Self Made'], \n",
|
|||
|
|
" 'Industry': ['Technology'], \n",
|
|||
|
|
"})\n",
|
|||
|
|
"\n",
|
|||
|
|
"predicted_networth = model_pipeline.predict(new_billionaire_data)\n",
|
|||
|
|
"print(f\"Прогнозируемое чистое состояние: {predicted_networth[0]}\")\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Подбор гиперпараметров методом поиска по сетке"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 44,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Fitting 3 folds for each of 36 candidates, totalling 108 fits\n",
|
|||
|
|
"Лучшие параметры: {'max_depth': 10, 'min_samples_split': 2, 'n_estimators': 200}\n",
|
|||
|
|
"Лучший результат (MSE): 5.631552208172496\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.pipeline import Pipeline\n",
|
|||
|
|
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
|
|||
|
|
"from sklearn.ensemble import RandomForestRegressor\n",
|
|||
|
|
"from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n",
|
|||
|
|
"from sklearn.compose import ColumnTransformer\n",
|
|||
|
|
"from sklearn.impute import SimpleImputer\n",
|
|||
|
|
"\n",
|
|||
|
|
"df = df.dropna()\n",
|
|||
|
|
"target = df['Networth']\n",
|
|||
|
|
"\n",
|
|||
|
|
"features = df.drop(columns=['Networth'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"features = features.drop(columns=['Name'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"num_columns = features.select_dtypes(include=['number']).columns\n",
|
|||
|
|
"cat_columns = features.select_dtypes(include=['object']).columns\n",
|
|||
|
|
"\n",
|
|||
|
|
"num_imputer = SimpleImputer(strategy=\"median\") \n",
|
|||
|
|
"num_scaler = StandardScaler()\n",
|
|||
|
|
"preprocessing_num = Pipeline(\n",
|
|||
|
|
" [\n",
|
|||
|
|
" (\"imputer\", num_imputer),\n",
|
|||
|
|
" (\"scaler\", num_scaler),\n",
|
|||
|
|
" ]\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\") \n",
|
|||
|
|
"cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
|
|||
|
|
"preprocessing_cat = Pipeline(\n",
|
|||
|
|
" [\n",
|
|||
|
|
" (\"imputer\", cat_imputer),\n",
|
|||
|
|
" (\"encoder\", cat_encoder),\n",
|
|||
|
|
" ]\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"features_preprocessing = ColumnTransformer(\n",
|
|||
|
|
" verbose_feature_names_out=False,\n",
|
|||
|
|
" transformers=[\n",
|
|||
|
|
" (\"preprocessing_num\", preprocessing_num, num_columns),\n",
|
|||
|
|
" (\"preprocessing_cat\", preprocessing_cat, cat_columns),\n",
|
|||
|
|
" ],\n",
|
|||
|
|
" remainder=\"passthrough\"\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"pipeline_end = Pipeline(\n",
|
|||
|
|
" [\n",
|
|||
|
|
" (\"features_preprocessing\", features_preprocessing),\n",
|
|||
|
|
" ]\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
|
|||
|
|
"\n",
|
|||
|
|
"X_train_processed = pipeline_end.fit_transform(X_train)\n",
|
|||
|
|
"X_test_processed = pipeline_end.transform(X_test)\n",
|
|||
|
|
"\n",
|
|||
|
|
"model = RandomForestRegressor()\n",
|
|||
|
|
"\n",
|
|||
|
|
"param_grid = {\n",
|
|||
|
|
" 'n_estimators': [50, 100, 200],\n",
|
|||
|
|
" 'max_depth': [None, 10, 20, 30], \n",
|
|||
|
|
" 'min_samples_split': [2, 5, 10] \n",
|
|||
|
|
"}\n",
|
|||
|
|
"\n",
|
|||
|
|
"grid_search = GridSearchCV(estimator=model, param_grid=param_grid,\n",
|
|||
|
|
" scoring='neg_mean_squared_error', cv=3, n_jobs=-1, verbose=2)\n",
|
|||
|
|
"\n",
|
|||
|
|
"grid_search.fit(X_train_processed, y_train)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Лучшие параметры:\", grid_search.best_params_)\n",
|
|||
|
|
"print(\"Лучший результат (MSE):\", -grid_search.best_score_) "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Обучение модели с новыми гиперпараметрами и сравнение новых и старых данных"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 45,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1, 2] during transform. These unknown categories will be encoded as all zeros\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Fitting 3 folds for each of 36 candidates, totalling 108 fits\n",
|
|||
|
|
"Старые параметры: {'max_depth': 30, 'min_samples_split': 2, 'n_estimators': 50}\n",
|
|||
|
|
"Лучший результат (MSE) на старых параметрах: 76.4137021557455\n",
|
|||
|
|
"\n",
|
|||
|
|
"Новые параметры: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
|
|||
|
|
"Лучший результат (MSE) на новых параметрах: 180.57283215031268\n",
|
|||
|
|
"Среднеквадратическая ошибка (MSE) на тестовых данных: 507.05822479642404\n",
|
|||
|
|
"Корень среднеквадратичной ошибки (RMSE) на тестовых данных: 22.517953388272748\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x500 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"from sklearn import metrics\n",
|
|||
|
|
"from sklearn.ensemble import RandomForestRegressor\n",
|
|||
|
|
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
|
|||
|
|
"from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n",
|
|||
|
|
"from sklearn.compose import ColumnTransformer\n",
|
|||
|
|
"from sklearn.impute import SimpleImputer\n",
|
|||
|
|
"from sklearn.pipeline import Pipeline\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"\n",
|
|||
|
|
"df = pd.read_csv(\"..//static//csv//Forbes Billionaires.csv\").head(100)\n",
|
|||
|
|
"\n",
|
|||
|
|
"df = df.dropna()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание целевой переменной (Networth)\n",
|
|||
|
|
"target = df['Networth']\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Удаление целевой переменной из исходных данных\n",
|
|||
|
|
"features = df.drop(columns=['Networth'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Удаление столбцов, которые не будут использоваться (например, имена)\n",
|
|||
|
|
"features = features.drop(columns=['Name'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Определение столбцов для обработки\n",
|
|||
|
|
"num_columns = features.select_dtypes(include=['number']).columns\n",
|
|||
|
|
"cat_columns = features.select_dtypes(include=['object']).columns\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Препроцессинг числовых столбцов\n",
|
|||
|
|
"num_imputer = SimpleImputer(strategy=\"median\") # Используем медиану для заполнения пропущенных значений в числовых столбцах\n",
|
|||
|
|
"num_scaler = StandardScaler()\n",
|
|||
|
|
"preprocessing_num = Pipeline(\n",
|
|||
|
|
" [\n",
|
|||
|
|
" (\"imputer\", num_imputer),\n",
|
|||
|
|
" (\"scaler\", num_scaler),\n",
|
|||
|
|
" ]\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Препроцессинг категориальных столбцов\n",
|
|||
|
|
"cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\") # Используем 'unknown' для заполнения пропущенных значений в категориальных столбцах\n",
|
|||
|
|
"cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
|
|||
|
|
"preprocessing_cat = Pipeline(\n",
|
|||
|
|
" [\n",
|
|||
|
|
" (\"imputer\", cat_imputer),\n",
|
|||
|
|
" (\"encoder\", cat_encoder),\n",
|
|||
|
|
" ]\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Объединение препроцессинга\n",
|
|||
|
|
"features_preprocessing = ColumnTransformer(\n",
|
|||
|
|
" verbose_feature_names_out=False,\n",
|
|||
|
|
" transformers=[\n",
|
|||
|
|
" (\"preprocessing_num\", preprocessing_num, num_columns),\n",
|
|||
|
|
" (\"preprocessing_cat\", preprocessing_cat, cat_columns),\n",
|
|||
|
|
" ],\n",
|
|||
|
|
" remainder=\"passthrough\"\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание финального пайплайна\n",
|
|||
|
|
"pipeline_end = Pipeline(\n",
|
|||
|
|
" [\n",
|
|||
|
|
" (\"features_preprocessing\", features_preprocessing),\n",
|
|||
|
|
" ]\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Разделение данных на обучающую и тестовую выборки\n",
|
|||
|
|
"X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Применение пайплайна к данным\n",
|
|||
|
|
"X_train_processed = pipeline_end.fit_transform(X_train)\n",
|
|||
|
|
"X_test_processed = pipeline_end.transform(X_test)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 1. Настройка параметров для старых значений\n",
|
|||
|
|
"old_param_grid = {\n",
|
|||
|
|
" 'n_estimators': [50, 100, 200], # Количество деревьев\n",
|
|||
|
|
" 'max_depth': [None, 10, 20, 30], # Максимальная глубина дерева\n",
|
|||
|
|
" 'min_samples_split': [2, 5, 10] # Минимальное количество образцов для разбиения узла\n",
|
|||
|
|
"}\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Подбор гиперпараметров с помощью Grid Search для старых параметров\n",
|
|||
|
|
"old_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
|
|||
|
|
" param_grid=old_param_grid,\n",
|
|||
|
|
" scoring='neg_mean_squared_error', cv=3, n_jobs=-1, verbose=2)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Обучение модели на тренировочных данных\n",
|
|||
|
|
"old_grid_search.fit(X_train_processed, y_train)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 2. Результаты подбора для старых параметров\n",
|
|||
|
|
"old_best_params = old_grid_search.best_params_\n",
|
|||
|
|
"old_best_mse = -old_grid_search.best_score_ # Меняем знак, так как берем отрицательное значение MSE\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 3. Настройка параметров для новых значений\n",
|
|||
|
|
"new_param_grid = {\n",
|
|||
|
|
" 'n_estimators': [200],\n",
|
|||
|
|
" 'max_depth': [10],\n",
|
|||
|
|
" 'min_samples_split': [10]\n",
|
|||
|
|
"}\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Подбор гиперпараметров с помощью Grid Search для новых параметров\n",
|
|||
|
|
"new_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
|
|||
|
|
" param_grid=new_param_grid,\n",
|
|||
|
|
" scoring='neg_mean_squared_error', cv=2)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Обучение модели на тренировочных данных\n",
|
|||
|
|
"new_grid_search.fit(X_train_processed, y_train)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 4. Результаты подбора для новых параметров\n",
|
|||
|
|
"new_best_params = new_grid_search.best_params_\n",
|
|||
|
|
"new_best_mse = -new_grid_search.best_score_ # Меняем знак, так как берем отрицательное значение MSE\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 5. Обучение модели с лучшими параметрами для новых значений\n",
|
|||
|
|
"model_best = RandomForestRegressor(**new_best_params)\n",
|
|||
|
|
"model_best.fit(X_train_processed, y_train)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Прогнозирование на тестовой выборке\n",
|
|||
|
|
"y_pred = model_best.predict(X_test_processed)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Оценка производительности модели\n",
|
|||
|
|
"mse = metrics.mean_squared_error(y_test, y_pred)\n",
|
|||
|
|
"rmse = np.sqrt(mse)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Вывод результатов\n",
|
|||
|
|
"print(\"Старые параметры:\", old_best_params)\n",
|
|||
|
|
"print(\"Лучший результат (MSE) на старых параметрах:\", old_best_mse)\n",
|
|||
|
|
"print(\"\\nНовые параметры:\", new_best_params)\n",
|
|||
|
|
"print(\"Лучший результат (MSE) на новых параметрах:\", new_best_mse)\n",
|
|||
|
|
"print(\"Среднеквадратическая ошибка (MSE) на тестовых данных:\", mse)\n",
|
|||
|
|
"print(\"Корень среднеквадратичной ошибки (RMSE) на тестовых данных:\", rmse)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Обучение модели с лучшими параметрами для старых значений\n",
|
|||
|
|
"model_old = RandomForestRegressor(**old_best_params)\n",
|
|||
|
|
"model_old.fit(X_train_processed, y_train)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Прогнозирование на тестовой выборке для старых параметров\n",
|
|||
|
|
"y_pred_old = model_old.predict(X_test_processed)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация ошибок\n",
|
|||
|
|
"plt.figure(figsize=(10, 5))\n",
|
|||
|
|
"plt.plot(y_test.values, label='Реальные значения', marker='o', linestyle='-', color='black')\n",
|
|||
|
|
"plt.plot(y_pred_old, label='Предсказанные значения (старые параметры)', marker='x', linestyle='--', color='blue')\n",
|
|||
|
|
"plt.plot(y_pred, label='Предсказанные значения (новые параметры)', marker='s', linestyle='--', color='orange')\n",
|
|||
|
|
"plt.xlabel('Объекты')\n",
|
|||
|
|
"plt.ylabel('Значения')\n",
|
|||
|
|
"plt.title('Сравнение реальных и предсказанных значений')\n",
|
|||
|
|
"plt.legend()\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Модель, обученная на новых параметрах, показала худший результат (MSE) на кросс-валидации, что указывает на ее меньшую точность по сравнению с моделью, обученной на старых параметрах. Однако, MSE на тестовых данных одинакова для обеих моделей, что говорит о том, что обе модели имеют одинаковую производительность на тестовых данных."
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.12.5"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|